{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import talib as  ta\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = \"F:\\HQData\\market_A\\\\\"  # 晶澳科技\n",
    "path_stock = path + '002459.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>highp</th>\n",
       "      <th>openp</th>\n",
       "      <th>lowp</th>\n",
       "      <th>nowv</th>\n",
       "      <th>preclose</th>\n",
       "      <th>curvol</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfo</th>\n",
       "      <th>bonusInfoType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-13</td>\n",
       "      <td>66.94</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.05</td>\n",
       "      <td>66.94</td>\n",
       "      <td>60.85</td>\n",
       "      <td>21497766.0</td>\n",
       "      <td>1.406617e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>62.40</td>\n",
       "      <td>60.21</td>\n",
       "      <td>58.90</td>\n",
       "      <td>60.85</td>\n",
       "      <td>60.26</td>\n",
       "      <td>14532780.0</td>\n",
       "      <td>8.833182e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>61.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>55.88</td>\n",
       "      <td>60.26</td>\n",
       "      <td>59.36</td>\n",
       "      <td>20632599.0</td>\n",
       "      <td>1.214406e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>67.40</td>\n",
       "      <td>67.00</td>\n",
       "      <td>59.36</td>\n",
       "      <td>59.36</td>\n",
       "      <td>65.95</td>\n",
       "      <td>19460143.0</td>\n",
       "      <td>1.185766e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>67.80</td>\n",
       "      <td>61.99</td>\n",
       "      <td>61.02</td>\n",
       "      <td>65.95</td>\n",
       "      <td>63.73</td>\n",
       "      <td>24245238.0</td>\n",
       "      <td>1.575408e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        times  highp  openp   lowp   nowv  preclose      curvol      curvalue  \\\n",
       "0  2021-10-13  66.94  62.20  61.05  66.94     60.85  21497766.0  1.406617e+09   \n",
       "1  2021-10-12  62.40  60.21  58.90  60.85     60.26  14532780.0  8.833182e+08   \n",
       "2  2021-10-11  61.00  59.00  55.88  60.26     59.36  20632599.0  1.214406e+09   \n",
       "3  2021-10-08  67.40  67.00  59.36  59.36     65.95  19460143.0  1.185766e+09   \n",
       "4  2021-09-30  67.80  61.99  61.02  65.95     63.73  24245238.0  1.575408e+09   \n",
       "\n",
       "   signType  dkWarnType bonusInfo  bonusInfoType  \n",
       "0         0           0       NaN              0  \n",
       "1         0           0       NaN              0  \n",
       "2         0           0       NaN              0  \n",
       "3         0           0       NaN              0  \n",
       "4         0           0       NaN              0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(path_stock)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x27896d74910>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x576 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,8))\n",
    "close = df[['times','nowv']]\n",
    "close = close.set_index('times').sort_index()\n",
    "close.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>times</th>\n",
       "      <th>highp</th>\n",
       "      <th>openp</th>\n",
       "      <th>lowp</th>\n",
       "      <th>nowv</th>\n",
       "      <th>preclose</th>\n",
       "      <th>curvol</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfo</th>\n",
       "      <th>bonusInfoType</th>\n",
       "      <th>ave_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-10-13</td>\n",
       "      <td>66.94</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.05</td>\n",
       "      <td>66.94</td>\n",
       "      <td>60.85</td>\n",
       "      <td>21497766.0</td>\n",
       "      <td>1.406617e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>65.430862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-10-12</td>\n",
       "      <td>62.40</td>\n",
       "      <td>60.21</td>\n",
       "      <td>58.90</td>\n",
       "      <td>60.85</td>\n",
       "      <td>60.26</td>\n",
       "      <td>14532780.0</td>\n",
       "      <td>8.833182e+08</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>60.781088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-10-11</td>\n",
       "      <td>61.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>55.88</td>\n",
       "      <td>60.26</td>\n",
       "      <td>59.36</td>\n",
       "      <td>20632599.0</td>\n",
       "      <td>1.214406e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>58.858590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-10-08</td>\n",
       "      <td>67.40</td>\n",
       "      <td>67.00</td>\n",
       "      <td>59.36</td>\n",
       "      <td>59.36</td>\n",
       "      <td>65.95</td>\n",
       "      <td>19460143.0</td>\n",
       "      <td>1.185766e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>60.933081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-09-30</td>\n",
       "      <td>67.80</td>\n",
       "      <td>61.99</td>\n",
       "      <td>61.02</td>\n",
       "      <td>65.95</td>\n",
       "      <td>63.73</td>\n",
       "      <td>24245238.0</td>\n",
       "      <td>1.575408e+09</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>64.978033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        times  highp  openp   lowp   nowv  preclose      curvol      curvalue  \\\n",
       "0  2021-10-13  66.94  62.20  61.05  66.94     60.85  21497766.0  1.406617e+09   \n",
       "1  2021-10-12  62.40  60.21  58.90  60.85     60.26  14532780.0  8.833182e+08   \n",
       "2  2021-10-11  61.00  59.00  55.88  60.26     59.36  20632599.0  1.214406e+09   \n",
       "3  2021-10-08  67.40  67.00  59.36  59.36     65.95  19460143.0  1.185766e+09   \n",
       "4  2021-09-30  67.80  61.99  61.02  65.95     63.73  24245238.0  1.575408e+09   \n",
       "\n",
       "   signType  dkWarnType bonusInfo  bonusInfoType  ave_price  \n",
       "0         0           0       NaN              0  65.430862  \n",
       "1         0           0       NaN              0  60.781088  \n",
       "2         0           0       NaN              0  58.858590  \n",
       "3         0           0       NaN              0  60.933081  \n",
       "4         0           0       NaN              0  64.978033  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['ave_price'] = df['curvalue'] / df['curvol']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1852e68dee0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = df.sort_index(ascending=False)\n",
    "df.ave_price.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2622    33.107935\n",
       "2621    34.475366\n",
       "2620    32.424192\n",
       "2619    30.632096\n",
       "2618    31.142064\n",
       "          ...    \n",
       "4       64.978033\n",
       "3       60.933081\n",
       "2       58.858590\n",
       "1       60.781088\n",
       "0       65.430862\n",
       "Name: ave_price, Length: 2623, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ave_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nowv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-08-10</th>\n",
       "      <td>36.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-11</th>\n",
       "      <td>34.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-12</th>\n",
       "      <td>31.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-13</th>\n",
       "      <td>30.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-16</th>\n",
       "      <td>31.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             nowv\n",
       "times            \n",
       "2010-08-10  36.50\n",
       "2010-08-11  34.09\n",
       "2010-08-12  31.20\n",
       "2010-08-13  30.80\n",
       "2010-08-16  31.50"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>66.94</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.05</td>\n",
       "      <td>66.94</td>\n",
       "      <td>60.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>62.40</td>\n",
       "      <td>60.21</td>\n",
       "      <td>58.90</td>\n",
       "      <td>60.85</td>\n",
       "      <td>60.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>61.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>55.88</td>\n",
       "      <td>60.26</td>\n",
       "      <td>59.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>67.40</td>\n",
       "      <td>67.00</td>\n",
       "      <td>59.36</td>\n",
       "      <td>59.36</td>\n",
       "      <td>65.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>67.80</td>\n",
       "      <td>61.99</td>\n",
       "      <td>61.02</td>\n",
       "      <td>65.95</td>\n",
       "      <td>63.73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high   open    low  close  preclose\n",
       "times                                           \n",
       "2021-10-13  66.94  62.20  61.05  66.94     60.85\n",
       "2021-10-12  62.40  60.21  58.90  60.85     60.26\n",
       "2021-10-11  61.00  59.00  55.88  60.26     59.36\n",
       "2021-10-08  67.40  67.00  59.36  59.36     65.95\n",
       "2021-09-30  67.80  61.99  61.02  65.95     63.73"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desc = df[['times','highp','openp','lowp','nowv','preclose']]\n",
    "df_desc = df_desc.rename(columns={\"highp\":\"high\", 'lowp':'low', 'nowv':'close', 'openp':'open'})\n",
    "df_desc = df_desc.set_index('times')\n",
    "df_desc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df_desc_open(df):\n",
    "    # columns = ['high', 'low', 'close', 'preclose','open']\n",
    "    df_desc = df\n",
    "    df_desc['gap_updown'] = df_desc['close'] - df_desc['preclose']\n",
    "    df_desc['rate_updown'] = (df_desc['close'] - df_desc['preclose'])/df_desc['preclose']\n",
    "    df_desc['abs_updown'] = df_desc['gap_updown'].abs()\n",
    "    df_desc['abs_updown_rate'] = df_desc['rate_updown'].abs()\n",
    "    \n",
    "    df_desc['gap_high_low'] = df_desc['high'] - df_desc['low']\n",
    "    df_desc['rate_high_low'] = (df_desc['high'] - df_desc['low'])/df_desc['low']\n",
    "     \n",
    "    df_desc['gap_high_open'] = df_desc['high'] - df_desc['open']\n",
    "    df_desc['rate_high_open'] = (df_desc['high'] - df_desc['open'])/df_desc['open']    \n",
    "    \n",
    "    df_desc['gap_open_low'] = df_desc['open'] - df_desc['low']\n",
    "    df_desc['rate_open_low'] = (df_desc['open'] - df_desc['low'])/df_desc['low']\n",
    "    \n",
    "    return df_desc\n",
    "\n",
    "def get_df_desc_close(df):\n",
    "    # columns = ['high', 'low', 'close', 'preclose']\n",
    "    df_desc = df\n",
    "    df_desc['gap_updown'] = df_desc['close'] - df_desc['preclose']\n",
    "    df_desc['rate_updown'] = (df_desc['close'] - df_desc['preclose'])/df_desc['preclose']\n",
    "    df_desc['abs_updown'] = df_desc['gap_updown'].abs()\n",
    "    df_desc['abs_updown_rate'] = df_desc['rate_updown'].abs()\n",
    "    \n",
    "    df_desc['gap_high_low'] = df_desc['high'] - df_desc['low']\n",
    "    df_desc['rate_high_low'] = (df_desc['high'] - df_desc['low'])/df_desc['low']\n",
    "     \n",
    "    df_desc['gap_high_close'] = df_desc['high'] - df_desc['close']\n",
    "    df_desc['rate_high_close'] = (df_desc['high'] - df_desc['close'])/df_desc['close']    \n",
    "    \n",
    "    df_desc['gap_close_low'] = df_desc['close'] - df_desc['low']\n",
    "    df_desc['rate_close_low'] = (df_desc['close'] - df_desc['low'])/df_desc['low']\n",
    "    \n",
    "    return df_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>open</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>preclose</th>\n",
       "      <th>gap_updown</th>\n",
       "      <th>rate_updown</th>\n",
       "      <th>abs_updown</th>\n",
       "      <th>abs_updown_rate</th>\n",
       "      <th>gap_high_low</th>\n",
       "      <th>rate_high_low</th>\n",
       "      <th>gap_high_open</th>\n",
       "      <th>rate_high_open</th>\n",
       "      <th>gap_open_low</th>\n",
       "      <th>rate_open_low</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>66.94</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.05</td>\n",
       "      <td>66.94</td>\n",
       "      <td>60.85</td>\n",
       "      <td>6.09</td>\n",
       "      <td>0.100082</td>\n",
       "      <td>6.09</td>\n",
       "      <td>0.100082</td>\n",
       "      <td>5.89</td>\n",
       "      <td>0.096478</td>\n",
       "      <td>4.74</td>\n",
       "      <td>0.076206</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.018837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>62.40</td>\n",
       "      <td>60.21</td>\n",
       "      <td>58.90</td>\n",
       "      <td>60.85</td>\n",
       "      <td>60.26</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.009791</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.009791</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.059423</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.036373</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.022241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>61.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>55.88</td>\n",
       "      <td>60.26</td>\n",
       "      <td>59.36</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.015162</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.015162</td>\n",
       "      <td>5.12</td>\n",
       "      <td>0.091625</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.033898</td>\n",
       "      <td>3.12</td>\n",
       "      <td>0.055834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>67.40</td>\n",
       "      <td>67.00</td>\n",
       "      <td>59.36</td>\n",
       "      <td>59.36</td>\n",
       "      <td>65.95</td>\n",
       "      <td>-6.59</td>\n",
       "      <td>-0.099924</td>\n",
       "      <td>6.59</td>\n",
       "      <td>0.099924</td>\n",
       "      <td>8.04</td>\n",
       "      <td>0.135445</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.005970</td>\n",
       "      <td>7.64</td>\n",
       "      <td>0.128706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>67.80</td>\n",
       "      <td>61.99</td>\n",
       "      <td>61.02</td>\n",
       "      <td>65.95</td>\n",
       "      <td>63.73</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.034834</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.034834</td>\n",
       "      <td>6.78</td>\n",
       "      <td>0.111111</td>\n",
       "      <td>5.81</td>\n",
       "      <td>0.093725</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.015896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high   open    low  close  preclose  gap_updown  rate_updown  \\\n",
       "times                                                                       \n",
       "2021-10-13  66.94  62.20  61.05  66.94     60.85        6.09     0.100082   \n",
       "2021-10-12  62.40  60.21  58.90  60.85     60.26        0.59     0.009791   \n",
       "2021-10-11  61.00  59.00  55.88  60.26     59.36        0.90     0.015162   \n",
       "2021-10-08  67.40  67.00  59.36  59.36     65.95       -6.59    -0.099924   \n",
       "2021-09-30  67.80  61.99  61.02  65.95     63.73        2.22     0.034834   \n",
       "\n",
       "            abs_updown  abs_updown_rate  gap_high_low  rate_high_low  \\\n",
       "times                                                                  \n",
       "2021-10-13        6.09         0.100082          5.89       0.096478   \n",
       "2021-10-12        0.59         0.009791          3.50       0.059423   \n",
       "2021-10-11        0.90         0.015162          5.12       0.091625   \n",
       "2021-10-08        6.59         0.099924          8.04       0.135445   \n",
       "2021-09-30        2.22         0.034834          6.78       0.111111   \n",
       "\n",
       "            gap_high_open  rate_high_open  gap_open_low  rate_open_low  \n",
       "times                                                                   \n",
       "2021-10-13           4.74        0.076206          1.15       0.018837  \n",
       "2021-10-12           2.19        0.036373          1.31       0.022241  \n",
       "2021-10-11           2.00        0.033898          3.12       0.055834  \n",
       "2021-10-08           0.40        0.005970          7.64       0.128706  \n",
       "2021-09-30           5.81        0.093725          0.97       0.015896  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tem = get_df_desc_open(df_desc)\n",
    "df_tem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>17.256413</td>\n",
       "      <td>10.887809</td>\n",
       "      <td>5.440000</td>\n",
       "      <td>11.280000</td>\n",
       "      <td>14.470000</td>\n",
       "      <td>18.440000</td>\n",
       "      <td>78.430000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>16.824396</td>\n",
       "      <td>10.487431</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>11.070000</td>\n",
       "      <td>14.140000</td>\n",
       "      <td>18.050000</td>\n",
       "      <td>78.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>16.433729</td>\n",
       "      <td>10.070805</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>10.895000</td>\n",
       "      <td>13.830000</td>\n",
       "      <td>17.680000</td>\n",
       "      <td>71.540000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>16.871960</td>\n",
       "      <td>10.552907</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>11.075000</td>\n",
       "      <td>14.170000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>77.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preclose</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>16.855067</td>\n",
       "      <td>10.508138</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>11.075000</td>\n",
       "      <td>14.170000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>77.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gap_updown</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.016893</td>\n",
       "      <td>0.845270</td>\n",
       "      <td>-6.770000</td>\n",
       "      <td>-0.190000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>14.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_updown</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.001014</td>\n",
       "      <td>0.035111</td>\n",
       "      <td>-0.273388</td>\n",
       "      <td>-0.014268</td>\n",
       "      <td>0.000501</td>\n",
       "      <td>0.016253</td>\n",
       "      <td>0.625111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abs_updown</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.433035</td>\n",
       "      <td>0.726069</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>14.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abs_updown_rate</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.022949</td>\n",
       "      <td>0.026589</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006096</td>\n",
       "      <td>0.015198</td>\n",
       "      <td>0.030424</td>\n",
       "      <td>0.625111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gap_high_low</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.822684</td>\n",
       "      <td>0.977662</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>8.380000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_high_low</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.045047</td>\n",
       "      <td>0.028317</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.024478</td>\n",
       "      <td>0.037478</td>\n",
       "      <td>0.058343</td>\n",
       "      <td>0.222883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gap_high_open</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.432017</td>\n",
       "      <td>0.690888</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>8.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_high_open</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.023662</td>\n",
       "      <td>0.024664</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006761</td>\n",
       "      <td>0.016260</td>\n",
       "      <td>0.032677</td>\n",
       "      <td>0.219259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gap_open_low</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.390667</td>\n",
       "      <td>0.661203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>7.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rate_open_low</th>\n",
       "      <td>2623.0</td>\n",
       "      <td>0.021052</td>\n",
       "      <td>0.022460</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.014451</td>\n",
       "      <td>0.027081</td>\n",
       "      <td>0.221914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean        std       min        25%        50%  \\\n",
       "high             2623.0  17.256413  10.887809  5.440000  11.280000  14.470000   \n",
       "open             2623.0  16.824396  10.487431  5.310000  11.070000  14.140000   \n",
       "low              2623.0  16.433729  10.070805  5.100000  10.895000  13.830000   \n",
       "close            2623.0  16.871960  10.552907  5.310000  11.075000  14.170000   \n",
       "preclose         2623.0  16.855067  10.508138  5.310000  11.075000  14.170000   \n",
       "gap_updown       2623.0   0.016893   0.845270 -6.770000  -0.190000   0.010000   \n",
       "rate_updown      2623.0   0.001014   0.035111 -0.273388  -0.014268   0.000501   \n",
       "abs_updown       2623.0   0.433035   0.726069  0.000000   0.080000   0.200000   \n",
       "abs_updown_rate  2623.0   0.022949   0.026589  0.000000   0.006096   0.015198   \n",
       "gap_high_low     2623.0   0.822684   0.977662  0.000000   0.280000   0.500000   \n",
       "rate_high_low    2623.0   0.045047   0.028317  0.000000   0.024478   0.037478   \n",
       "gap_high_open    2623.0   0.432017   0.690888  0.000000   0.080000   0.210000   \n",
       "rate_high_open   2623.0   0.023662   0.024664  0.000000   0.006761   0.016260   \n",
       "gap_open_low     2623.0   0.390667   0.661203  0.000000   0.080000   0.190000   \n",
       "rate_open_low    2623.0   0.021052   0.022460  0.000000   0.006399   0.014451   \n",
       "\n",
       "                       75%        max  \n",
       "high             18.440000  78.430000  \n",
       "open             18.050000  78.000000  \n",
       "low              17.680000  71.540000  \n",
       "close            18.100000  77.800000  \n",
       "preclose         18.100000  77.800000  \n",
       "gap_updown        0.220000  14.040000  \n",
       "rate_updown       0.016253   0.625111  \n",
       "abs_updown        0.480000  14.040000  \n",
       "abs_updown_rate   0.030424   0.625111  \n",
       "gap_high_low      0.950000   8.380000  \n",
       "rate_high_low     0.058343   0.222883  \n",
       "gap_high_open     0.490000   8.320000  \n",
       "rate_high_open    0.032677   0.219259  \n",
       "gap_open_low      0.420000   7.980000  \n",
       "rate_open_low     0.027081   0.221914  "
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tem.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
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       "      <th>high</th>\n",
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       "      <th>times</th>\n",
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       "      <th>2020-11-09</th>\n",
       "      <td>32.83</td>\n",
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       "      <td>36.48</td>\n",
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       "      <th>2019-01-22</th>\n",
       "      <td>10.31</td>\n",
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       "      <td>8.52</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2018-11-07</th>\n",
       "      <td>16.02</td>\n",
       "      <td>16.02</td>\n",
       "      <td>16.02</td>\n",
       "      <td>16.02</td>\n",
       "      <td>14.56</td>\n",
       "      <td>1.46</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2018-11-06</th>\n",
       "      <td>14.56</td>\n",
       "      <td>14.56</td>\n",
       "      <td>14.56</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2018-11-05</th>\n",
       "      <td>13.24</td>\n",
       "      <td>13.24</td>\n",
       "      <td>13.24</td>\n",
       "      <td>13.24</td>\n",
       "      <td>12.04</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.099668</td>\n",
       "      <td>1.20</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>2015-07-07</th>\n",
       "      <td>16.62</td>\n",
       "      <td>16.62</td>\n",
       "      <td>16.62</td>\n",
       "      <td>16.62</td>\n",
       "      <td>18.47</td>\n",
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       "      <th>2011-12-16</th>\n",
       "      <td>12.03</td>\n",
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       "             high   open    low  close  preclose  gap_updown  rate_updown  \\\n",
       "times                                                                       \n",
       "2020-11-09  32.83  32.83  32.83  32.83     36.48       -3.65    -0.100055   \n",
       "2019-01-22  10.31  10.31  10.31  10.31      9.37        0.94     0.100320   \n",
       "2019-01-21   9.37   9.37   9.37   9.37      8.52        0.85     0.099765   \n",
       "2018-11-07  16.02  16.02  16.02  16.02     14.56        1.46     0.100275   \n",
       "2018-11-06  14.56  14.56  14.56  14.56     13.24        1.32     0.099698   \n",
       "2018-11-05  13.24  13.24  13.24  13.24     12.04        1.20     0.099668   \n",
       "2015-07-07  16.62  16.62  16.62  16.62     18.47       -1.85    -0.100162   \n",
       "2011-12-16  12.03  12.03  12.03  12.03     13.37       -1.34    -0.100224   \n",
       "\n",
       "            abs_updown  abs_updown_rate  gap_high_low  rate_high_low  \\\n",
       "times                                                                  \n",
       "2020-11-09        3.65         0.100055           0.0            0.0   \n",
       "2019-01-22        0.94         0.100320           0.0            0.0   \n",
       "2019-01-21        0.85         0.099765           0.0            0.0   \n",
       "2018-11-07        1.46         0.100275           0.0            0.0   \n",
       "2018-11-06        1.32         0.099698           0.0            0.0   \n",
       "2018-11-05        1.20         0.099668           0.0            0.0   \n",
       "2015-07-07        1.85         0.100162           0.0            0.0   \n",
       "2011-12-16        1.34         0.100224           0.0            0.0   \n",
       "\n",
       "            gap_high_open  rate_high_open  gap_open_low  rate_open_low  \n",
       "times                                                                   \n",
       "2020-11-09            0.0             0.0           0.0            0.0  \n",
       "2019-01-22            0.0             0.0           0.0            0.0  \n",
       "2019-01-21            0.0             0.0           0.0            0.0  \n",
       "2018-11-07            0.0             0.0           0.0            0.0  \n",
       "2018-11-06            0.0             0.0           0.0            0.0  \n",
       "2018-11-05            0.0             0.0           0.0            0.0  \n",
       "2015-07-07            0.0             0.0           0.0            0.0  \n",
       "2011-12-16            0.0             0.0           0.0            0.0  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tem[df_tem.gap_high_low == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2010-08-10</th>\n",
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       "      <td>31.00</td>\n",
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       "      <th>2021-09-16</th>\n",
       "      <td>66.40</td>\n",
       "      <td>66.40</td>\n",
       "      <td>60.97</td>\n",
       "      <td>60.97</td>\n",
       "      <td>67.74</td>\n",
       "      <td>-6.77</td>\n",
       "      <td>5.43</td>\n",
       "      <td>6.77</td>\n",
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       "      <th>2021-10-08</th>\n",
       "      <td>67.40</td>\n",
       "      <td>67.00</td>\n",
       "      <td>59.36</td>\n",
       "      <td>59.36</td>\n",
       "      <td>65.95</td>\n",
       "      <td>-6.59</td>\n",
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       "      <td>6.59</td>\n",
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       "      <th>2021-08-25</th>\n",
       "      <td>68.20</td>\n",
       "      <td>61.19</td>\n",
       "      <td>61.00</td>\n",
       "      <td>68.20</td>\n",
       "      <td>62.00</td>\n",
       "      <td>6.20</td>\n",
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       "      <th>2021-10-13</th>\n",
       "      <td>66.94</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.05</td>\n",
       "      <td>66.94</td>\n",
       "      <td>60.85</td>\n",
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       "      <th>2011-10-10</th>\n",
       "      <td>14.09</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.66</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>2012-12-28</th>\n",
       "      <td>6.36</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.26</td>\n",
       "      <td>6.32</td>\n",
       "      <td>6.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>2018-04-13</th>\n",
       "      <td>12.76</td>\n",
       "      <td>12.70</td>\n",
       "      <td>12.38</td>\n",
       "      <td>12.46</td>\n",
       "      <td>12.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>2014-03-31</th>\n",
       "      <td>8.33</td>\n",
       "      <td>8.15</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.12</td>\n",
       "      <td>8.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
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       "      <th>2012-12-24</th>\n",
       "      <td>6.09</td>\n",
       "      <td>6.06</td>\n",
       "      <td>6.01</td>\n",
       "      <td>6.07</td>\n",
       "      <td>6.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
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       "<p>2623 rows × 8 columns</p>\n",
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      "text/plain": [
       "            highp  openp   lowp   nowv  preclose  updown   gap  abs_updown\n",
       "times                                                                     \n",
       "2010-08-10  37.25  31.00  31.00  36.50     22.46   14.04  6.25       14.04\n",
       "2021-09-16  66.40  66.40  60.97  60.97     67.74   -6.77  5.43        6.77\n",
       "2021-10-08  67.40  67.00  59.36  59.36     65.95   -6.59  8.04        6.59\n",
       "2021-08-25  68.20  61.19  61.00  68.20     62.00    6.20  7.20        6.20\n",
       "2021-10-13  66.94  62.20  61.05  66.94     60.85    6.09  5.89        6.09\n",
       "...           ...    ...    ...    ...       ...     ...   ...         ...\n",
       "2011-10-10  14.09  13.84  13.66  13.84     13.84    0.00  0.43        0.00\n",
       "2012-12-28   6.36   6.26   6.26   6.32      6.32    0.00  0.10        0.00\n",
       "2018-04-13  12.76  12.70  12.38  12.46     12.46    0.00  0.38        0.00\n",
       "2014-03-31   8.33   8.15   8.10   8.12      8.12    0.00  0.23        0.00\n",
       "2012-12-24   6.09   6.06   6.01   6.07      6.07    0.00  0.08        0.00\n",
       "\n",
       "[2623 rows x 8 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_desc.sort_values('abs_updown', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-29</th>\n",
       "      <td>9.87</td>\n",
       "      <td>9.66</td>\n",
       "      <td>9.25</td>\n",
       "      <td>9.25</td>\n",
       "      <td>11.42</td>\n",
       "      <td>-2.17</td>\n",
       "      <td>0.62</td>\n",
       "      <td>2.17</td>\n",
       "      <td>-0.234595</td>\n",
       "      <td>0.234595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-12-02</th>\n",
       "      <td>7.50</td>\n",
       "      <td>7.50</td>\n",
       "      <td>7.08</td>\n",
       "      <td>7.08</td>\n",
       "      <td>7.87</td>\n",
       "      <td>-0.79</td>\n",
       "      <td>0.42</td>\n",
       "      <td>0.79</td>\n",
       "      <td>-0.111582</td>\n",
       "      <td>0.111582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-17</th>\n",
       "      <td>15.07</td>\n",
       "      <td>14.97</td>\n",
       "      <td>13.55</td>\n",
       "      <td>13.55</td>\n",
       "      <td>15.06</td>\n",
       "      <td>-1.51</td>\n",
       "      <td>1.52</td>\n",
       "      <td>1.51</td>\n",
       "      <td>-0.111439</td>\n",
       "      <td>0.111439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-10</th>\n",
       "      <td>14.09</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.66</td>\n",
       "      <td>13.84</td>\n",
       "      <td>13.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-22</th>\n",
       "      <td>16.99</td>\n",
       "      <td>16.95</td>\n",
       "      <td>16.74</td>\n",
       "      <td>16.80</td>\n",
       "      <td>16.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-30</th>\n",
       "      <td>16.93</td>\n",
       "      <td>16.84</td>\n",
       "      <td>16.63</td>\n",
       "      <td>16.84</td>\n",
       "      <td>16.84</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-12</th>\n",
       "      <td>15.95</td>\n",
       "      <td>15.51</td>\n",
       "      <td>15.51</td>\n",
       "      <td>15.67</td>\n",
       "      <td>15.67</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.44</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-28</th>\n",
       "      <td>46.35</td>\n",
       "      <td>44.67</td>\n",
       "      <td>43.85</td>\n",
       "      <td>44.70</td>\n",
       "      <td>44.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2623 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            highp  openp   lowp   nowv  preclose  updown   gap  abs_updown  \\\n",
       "times                                                                        \n",
       "2010-08-10  37.25  31.00  31.00  36.50     22.46   14.04  6.25       14.04   \n",
       "2011-05-24  16.63  16.63  15.91  16.00     22.02   -6.02  0.72        6.02   \n",
       "2012-03-29   9.87   9.66   9.25   9.25     11.42   -2.17  0.62        2.17   \n",
       "2013-12-02   7.50   7.50   7.08   7.08      7.87   -0.79  0.42        0.79   \n",
       "2017-07-17  15.07  14.97  13.55  13.55     15.06   -1.51  1.52        1.51   \n",
       "...           ...    ...    ...    ...       ...     ...   ...         ...   \n",
       "2011-10-10  14.09  13.84  13.66  13.84     13.84    0.00  0.43        0.00   \n",
       "2016-09-22  16.99  16.95  16.74  16.80     16.80    0.00  0.25        0.00   \n",
       "2016-08-30  16.93  16.84  16.63  16.84     16.84    0.00  0.30        0.00   \n",
       "2016-08-12  15.95  15.51  15.51  15.67     15.67    0.00  0.44        0.00   \n",
       "2021-06-28  46.35  44.67  43.85  44.70     44.70    0.00  2.50        0.00   \n",
       "\n",
       "            updown_rate  abs_updown_rate  \n",
       "times                                     \n",
       "2010-08-10     0.384658         0.384658  \n",
       "2011-05-24    -0.376250         0.376250  \n",
       "2012-03-29    -0.234595         0.234595  \n",
       "2013-12-02    -0.111582         0.111582  \n",
       "2017-07-17    -0.111439         0.111439  \n",
       "...                 ...              ...  \n",
       "2011-10-10     0.000000         0.000000  \n",
       "2016-09-22     0.000000         0.000000  \n",
       "2016-08-30     0.000000         0.000000  \n",
       "2016-08-12     0.000000         0.000000  \n",
       "2021-06-28     0.000000         0.000000  \n",
       "\n",
       "[2623 rows x 10 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_desc.sort_values('abs_updown_rate', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a65c6760>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.boxplot(df_desc.abs_updown_rate)\n",
    "sns.distplot(df_2021.abs_updown_rate)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1972.000000\n",
       "mean        0.023087\n",
       "std         0.023968\n",
       "min         0.000287\n",
       "25%         0.006189\n",
       "50%         0.015009\n",
       "75%         0.030434\n",
       "max         0.111582\n",
       "Name: abs_updown_rate, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021[df_2021.abs_updown_rate > 0].abs_updown_rate.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highp</th>\n",
       "      <th>openp</th>\n",
       "      <th>lowp</th>\n",
       "      <th>nowv</th>\n",
       "      <th>preclose</th>\n",
       "      <th>updown</th>\n",
       "      <th>gap</th>\n",
       "      <th>abs_updown</th>\n",
       "      <th>updown_rate</th>\n",
       "      <th>abs_updown_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>66.94</td>\n",
       "      <td>62.20</td>\n",
       "      <td>61.05</td>\n",
       "      <td>66.94</td>\n",
       "      <td>60.85</td>\n",
       "      <td>6.09</td>\n",
       "      <td>5.89</td>\n",
       "      <td>6.09</td>\n",
       "      <td>0.090977</td>\n",
       "      <td>0.090977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>62.40</td>\n",
       "      <td>60.21</td>\n",
       "      <td>58.90</td>\n",
       "      <td>60.85</td>\n",
       "      <td>60.26</td>\n",
       "      <td>0.59</td>\n",
       "      <td>3.50</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.009696</td>\n",
       "      <td>0.009696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>61.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>55.88</td>\n",
       "      <td>60.26</td>\n",
       "      <td>59.36</td>\n",
       "      <td>0.90</td>\n",
       "      <td>5.12</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.014935</td>\n",
       "      <td>0.014935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>67.40</td>\n",
       "      <td>67.00</td>\n",
       "      <td>59.36</td>\n",
       "      <td>59.36</td>\n",
       "      <td>65.95</td>\n",
       "      <td>-6.59</td>\n",
       "      <td>8.04</td>\n",
       "      <td>6.59</td>\n",
       "      <td>-0.111018</td>\n",
       "      <td>0.111018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>67.80</td>\n",
       "      <td>61.99</td>\n",
       "      <td>61.02</td>\n",
       "      <td>65.95</td>\n",
       "      <td>63.73</td>\n",
       "      <td>2.22</td>\n",
       "      <td>6.78</td>\n",
       "      <td>2.22</td>\n",
       "      <td>0.033662</td>\n",
       "      <td>0.033662</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            highp  openp   lowp   nowv  preclose  updown   gap  abs_updown  \\\n",
       "times                                                                        \n",
       "2021-10-13  66.94  62.20  61.05  66.94     60.85    6.09  5.89        6.09   \n",
       "2021-10-12  62.40  60.21  58.90  60.85     60.26    0.59  3.50        0.59   \n",
       "2021-10-11  61.00  59.00  55.88  60.26     59.36    0.90  5.12        0.90   \n",
       "2021-10-08  67.40  67.00  59.36  59.36     65.95   -6.59  8.04        6.59   \n",
       "2021-09-30  67.80  61.99  61.02  65.95     63.73    2.22  6.78        2.22   \n",
       "\n",
       "            updown_rate  abs_updown_rate  \n",
       "times                                     \n",
       "2021-10-13     0.090977         0.090977  \n",
       "2021-10-12     0.009696         0.009696  \n",
       "2021-10-11     0.014935         0.014935  \n",
       "2021-10-08    -0.111018         0.111018  \n",
       "2021-09-30     0.033662         0.033662  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_2021.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x27899240b80>,\n",
       "  <matplotlib.lines.Line2D at 0x27899240ee0>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x2789925e280>,\n",
       "  <matplotlib.lines.Line2D at 0x2789925e5e0>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x27899240820>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x2789925e940>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x2789925ec40>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_abs_ud = df_desc.abs_updown_rate[df_desc['abs_updown_rate'] > 0]\n",
    "plt.boxplot(df_abs_ud)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_boll(df):\n",
    "    k = 2\n",
    "    df['MB'] = df['close'].rolling(20).mean()\n",
    "    df['std'] = df['close'].rolling(20).std()\n",
    "    df['UP'] = df ['MB'] + k * df['std']\n",
    "    df['DOWN'] = df['MB'] - k * df['std']\n",
    "    \n",
    "    # df.set_index(['date'], inplace=True)\n",
    "    \n",
    "    last_close = df['close'].shift(1)\n",
    "    shifted_up = df[\"UP\"].shift(1)\n",
    "    df['up_mark'] = (last_close <= shifted_up) & (df['close'] > shifted_up)\n",
    "    \n",
    "    shifted_down = df['DOWN'].shift(1)\n",
    "    df['down_mark'] = (last_close >= shifted_down) & (df['close'] < shifted_down)\n",
    "    return df\n",
    "\n",
    "def compute_rsi(df):\n",
    "    df['pre_close'] = df['close'].shift(1)\n",
    "    df['change_pct'] = (df['close'] - df['pre_close']) * 100 / df['pre_close']\n",
    "    df['up_pct'] = pd.DataFrame({'up_pct': df['change_pct'], 'zero':0}).max(1)\n",
    "    \n",
    "    # 计算RSI\n",
    "    df['RSI'] = df['up_pct'].rolling(N).mean() / abs(df['change_pct']).rolling(N).mean() * 100\n",
    "    \n",
    "    df['prev_rsi'] = df['RSI'].shift(1)\n",
    "    \n",
    "    # 超买 RSI下穿 80， 作为卖出信号\n",
    "    df_over_bought = df[(df['RSI'] < 80) & (df['prev_rsi'] >= 80)]\n",
    "    \n",
    "    # 超卖 RSI下上穿 20 ， 作为卖出信号\n",
    "    df_over_sold = df[(df['rsi'] > 20) & df['prev_rsi'] <= 20]\n",
    "\n",
    "def compute_fenxing(df):\n",
    "    df_shift_1 = df.shift(1)\n",
    "    df_shift_2 = df.shift(2)\n",
    "    df_shift_3 = df.shift(3)\n",
    "    df_shift_4 = df.shift(4)\n",
    "    # 顶分型  中间日的最高价 即大于前两天的最高价， 也大于后两天的最高价\n",
    "    df['up'] = (df_shift_2['high'] > df['high']) & \\\n",
    "                (df_shift_2['high'] > df_shift_1['high']) & \\\n",
    "                (df_shift_2['high'] > df_shift_3['high']) & \\\n",
    "                (df_shift_2['high'] > df_shift_4['high'])\n",
    "        \n",
    "    \n",
    "    # 底分型： 中间日的最低价， 即小于前两天的最低价， 也小于后两天的最低价\n",
    "    df['down'] = (df_shift_2['low'] < df['low']) & \\\n",
    "                 (df_shift_2['low'] < df_shift_1['low']) & \\\n",
    "                 (df_shift_2['low'] < df_shift_3['low']) & \\\n",
    "                 (df_shift_2['low'] < df_shift_4['low'])\n",
    "\n",
    "    df = df[(df['up'] | df['down'])]\n",
    "    return df\n",
    "\n",
    "\n",
    "def get_df_smas(df, lenthlist):\n",
    "    df_temp = pd.DataFrame(df['close'])\n",
    "    for i in lenthlist:\n",
    "        df_temp['sma_'+ str(i)] = ta.SMA(df['close'], i)\n",
    "    \n",
    "    return df_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>65.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>59.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>60.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>60.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>66.94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close\n",
       "times            \n",
       "2021-09-30  65.95\n",
       "2021-10-08  59.36\n",
       "2021-10-11  60.26\n",
       "2021-10-12  60.85\n",
       "2021-10-13  66.94"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recent = df_desc.rename(columns={\"nowv\":\"close\"})[['close']]\n",
    "df_recent = df_recent[df_recent.index > '2020-01-01'].sort_index(ascending=True)\n",
    "\n",
    "df_recent.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>MB</th>\n",
       "      <th>std</th>\n",
       "      <th>UP</th>\n",
       "      <th>DOWN</th>\n",
       "      <th>up_mark</th>\n",
       "      <th>down_mark</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>66.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>60.85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>60.26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-08</th>\n",
       "      <td>59.36</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-30</th>\n",
       "      <td>65.95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close  MB  std  UP  DOWN  up_mark  down_mark\n",
       "times                                                   \n",
       "2021-10-13  66.94 NaN  NaN NaN   NaN    False      False\n",
       "2021-10-12  60.85 NaN  NaN NaN   NaN    False      False\n",
       "2021-10-11  60.26 NaN  NaN NaN   NaN    False      False\n",
       "2021-10-08  59.36 NaN  NaN NaN   NaN    False      False\n",
       "2021-09-30  65.95 NaN  NaN NaN   NaN    False      False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_boll = compute_boll(df_desc.rename(columns={\"nowv\":\"close\"})[['close']])\n",
    "df_boll.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1152x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_boll = df_boll.sort_index(ascending=True)\n",
    "df_boll[['close', 'MB', 'UP', 'DOWN']].plot(figsize=(16, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>MB</th>\n",
       "      <th>std</th>\n",
       "      <th>UP</th>\n",
       "      <th>DOWN</th>\n",
       "      <th>up_mark</th>\n",
       "      <th>down_mark</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>11.29</td>\n",
       "      <td>12.9020</td>\n",
       "      <td>0.903616</td>\n",
       "      <td>14.709232</td>\n",
       "      <td>11.094768</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>11.73</td>\n",
       "      <td>13.1220</td>\n",
       "      <td>1.018779</td>\n",
       "      <td>15.159559</td>\n",
       "      <td>11.084441</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>11.84</td>\n",
       "      <td>13.3480</td>\n",
       "      <td>1.182004</td>\n",
       "      <td>15.712007</td>\n",
       "      <td>10.983993</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>12.79</td>\n",
       "      <td>13.5965</td>\n",
       "      <td>1.357665</td>\n",
       "      <td>16.311831</td>\n",
       "      <td>10.881169</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>12.75</td>\n",
       "      <td>13.7980</td>\n",
       "      <td>1.520912</td>\n",
       "      <td>16.839825</td>\n",
       "      <td>10.756175</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close       MB       std         UP       DOWN  up_mark  down_mark\n",
       "times                                                                         \n",
       "2020-01-02  11.29  12.9020  0.903616  14.709232  11.094768    False      False\n",
       "2020-01-03  11.73  13.1220  1.018779  15.159559  11.084441    False      False\n",
       "2020-01-06  11.84  13.3480  1.182004  15.712007  10.983993    False      False\n",
       "2020-01-07  12.79  13.5965  1.357665  16.311831  10.881169    False      False\n",
       "2020-01-08  12.75  13.7980  1.520912  16.839825  10.756175    False      False"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tem = df_boll[df_boll.index > '2020-01-01']\n",
    "df_tem.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tem[['close', 'MB', 'UP', 'DOWN']].plot(figsize=(16, 8))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>close</th>\n",
       "      <th>sma_5</th>\n",
       "      <th>sma_30</th>\n",
       "      <th>sma_60</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>times</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-02</th>\n",
       "      <td>11.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>11.73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>11.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>12.79</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-08</th>\n",
       "      <td>12.75</td>\n",
       "      <td>12.08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            close  sma_5  sma_30  sma_60\n",
       "times                                   \n",
       "2020-01-02  11.29    NaN     NaN     NaN\n",
       "2020-01-03  11.73    NaN     NaN     NaN\n",
       "2020-01-06  11.84    NaN     NaN     NaN\n",
       "2020-01-07  12.79    NaN     NaN     NaN\n",
       "2020-01-08  12.75  12.08     NaN     NaN"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sma = get_df_smas(df_recent, [5,30, 60])\n",
    "df_sma.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2789ed7aa90>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_sma.plot(figsize=(16, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "times               0\n",
       "highp               0\n",
       "openp               0\n",
       "lowp                0\n",
       "nowv                0\n",
       "preclose            0\n",
       "curvol              0\n",
       "curvalue            0\n",
       "signType            0\n",
       "dkWarnType          0\n",
       "bonusInfo        2621\n",
       "bonusInfoType       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>highp</th>\n",
       "      <th>openp</th>\n",
       "      <th>lowp</th>\n",
       "      <th>nowv</th>\n",
       "      <th>preclose</th>\n",
       "      <th>curvol</th>\n",
       "      <th>curvalue</th>\n",
       "      <th>signType</th>\n",
       "      <th>dkWarnType</th>\n",
       "      <th>bonusInfoType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2623.000000</td>\n",
       "      <td>2623.000000</td>\n",
       "      <td>2623.000000</td>\n",
       "      <td>2623.000000</td>\n",
       "      <td>2623.000000</td>\n",
       "      <td>2.623000e+03</td>\n",
       "      <td>2.623000e+03</td>\n",
       "      <td>2623.0</td>\n",
       "      <td>2623.000000</td>\n",
       "      <td>2623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17.256413</td>\n",
       "      <td>16.824396</td>\n",
       "      <td>16.433729</td>\n",
       "      <td>16.871960</td>\n",
       "      <td>16.855067</td>\n",
       "      <td>6.740781e+06</td>\n",
       "      <td>1.488588e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.056805</td>\n",
       "      <td>0.000762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.887809</td>\n",
       "      <td>10.487431</td>\n",
       "      <td>10.070805</td>\n",
       "      <td>10.552907</td>\n",
       "      <td>10.508138</td>\n",
       "      <td>7.834353e+06</td>\n",
       "      <td>2.694086e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.302882</td>\n",
       "      <td>0.027608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.440000</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>5.310000</td>\n",
       "      <td>2.246920e+05</td>\n",
       "      <td>3.166588e+06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.280000</td>\n",
       "      <td>11.070000</td>\n",
       "      <td>10.895000</td>\n",
       "      <td>11.075000</td>\n",
       "      <td>11.075000</td>\n",
       "      <td>1.919295e+06</td>\n",
       "      <td>2.301682e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.470000</td>\n",
       "      <td>14.140000</td>\n",
       "      <td>13.830000</td>\n",
       "      <td>14.170000</td>\n",
       "      <td>14.170000</td>\n",
       "      <td>3.400198e+06</td>\n",
       "      <td>4.547866e+07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.440000</td>\n",
       "      <td>18.050000</td>\n",
       "      <td>17.680000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>8.423125e+06</td>\n",
       "      <td>1.263238e+08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>78.430000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>71.540000</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>77.800000</td>\n",
       "      <td>7.970467e+07</td>\n",
       "      <td>2.292147e+09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             highp        openp         lowp         nowv     preclose  \\\n",
       "count  2623.000000  2623.000000  2623.000000  2623.000000  2623.000000   \n",
       "mean     17.256413    16.824396    16.433729    16.871960    16.855067   \n",
       "std      10.887809    10.487431    10.070805    10.552907    10.508138   \n",
       "min       5.440000     5.310000     5.100000     5.310000     5.310000   \n",
       "25%      11.280000    11.070000    10.895000    11.075000    11.075000   \n",
       "50%      14.470000    14.140000    13.830000    14.170000    14.170000   \n",
       "75%      18.440000    18.050000    17.680000    18.100000    18.100000   \n",
       "max      78.430000    78.000000    71.540000    77.800000    77.800000   \n",
       "\n",
       "             curvol      curvalue  signType   dkWarnType  bonusInfoType  \n",
       "count  2.623000e+03  2.623000e+03    2623.0  2623.000000    2623.000000  \n",
       "mean   6.740781e+06  1.488588e+08       0.0     0.056805       0.000762  \n",
       "std    7.834353e+06  2.694086e+08       0.0     0.302882       0.027608  \n",
       "min    2.246920e+05  3.166588e+06       0.0     0.000000       0.000000  \n",
       "25%    1.919295e+06  2.301682e+07       0.0     0.000000       0.000000  \n",
       "50%    3.400198e+06  4.547866e+07       0.0     0.000000       0.000000  \n",
       "75%    8.423125e+06  1.263238e+08       0.0     0.000000       0.000000  \n",
       "max    7.970467e+07  2.292147e+09       0.0     2.000000       1.000000  "
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a122dd30>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "close['nowv'].value_counts().sort_index().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x278a51a1df0>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(close)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "原版海龟ATR的计算方法\n",
    "真实波幅 = max(H-L, H-PDC, PDC-L)\n",
    "H=当日最高价\n",
    "L=当日最低价\n",
    "PDC=前一日收盘价\n",
    "N = (19 * PDN +TR)/20\n",
    "PDN = 前一日的N值\n",
    "TR= 当日的真实波动幅度\n",
    "由于公式中需要前一日的N值，你在首次计算N的时候不能用这个公式，只能计算真实波动幅度的20日简单平均值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def  getATR():\n",
    "    "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
